Performance evaluation of destriping algorithms: a test procedure based on simulated images

Striping noise is intrinsic to the process of image acquisition via scanning systems. Although it can be mitigated by radiometric calibration, the residual noise can jeopardize the quantitative analysis of the images and the extraction of physical parameters. This paper presents a new approach for evaluating destriping algorithms by means of a synergic use of: (1) distortion measurements, (2) quality indexes and relevant benchmark values calculated from simulated data, and (3) evaluation of effects on parameters extraction. The proposed procedure is here used to evaluate the performance of three destriping algorithms: Destriping Algorithm based on Statistics and Filtering (DASF), ENVI (R) algorithm and Wavelet-Fourier Filtering (WFF), on two different scenarios of hyperspectral push-broom data. The study pointed out the need of an approach based on the analysis of several aspects in order to identify the best performing algorithm. In addition, the results showed a good performance of both DASF and WFF that preserved the original radiometry without introducing spectral distortion.